Ethics in Machine Learning

October 30, 2017

Rise of the Machines

Sentient AI overlords are a ways off
There are real concerns we need to be thinking of right now

Unintended Consequences

Case Study: Microsoft Tay

Case Study: Microsoft Tay

Now, you might wonder why Microsoft would unleash a bot upon the world that was so unhinged. Well it looks like the company just underestimated how unpleasant many people are on social media.

– Ashley Rodriguez, Quartz

Model training error
Data quality issue
Narrow focus on technology while neglecting actual users

Biased Data

In June of 2015, Google found itself in an embarrassing situation when its new photo tagging algorithm mistakenly categorized an black couple as gorillas. The engineers responded quickly and apologetically, but the bias had shown through; there weren't enough people of color in the training data for the algorithm to correctly categorize the photo. The lack of diversity in machine learning training data will continue to be a major ethics issue as it manifests itself in ways the developers couldn't imagine.

References: https://www.theverge.com/2015/7/1/8880363/google-apologizes-photos-app-tags-two-black-people-gorillas https://www.buzzfeed.com/syreetamcfadden/teaching-the-camera-to-see-my-skin?utm_term=.auDmMv84r#.agQaz5W6O http://content.time.com/time/business/article/0,8599,1954643,00.html

Biased Data

Safety Concerns

Intentional Harm

Bullet 1
Turn off alarm
Get out of bed